This notebook is totally similar to Analysis.Rmd but it also includes the patients with a thyroid cancer.
This notebook is associated with Article A, the main points of which are outlined here. All the result figures of the article in question can be generated using the code present in this notebook. Please refer to the article itself for more details about the methods.
The aim of the study is to investigate the mechanisms of resistance to BRAF inhibition in BRAF-mutated cancers. In particular, we are interested in colorectal cancer and melanoma which show different sensitivities despite some molecular similarities.
Graphical abstract of the complete study
For this purpose, a generic logical model summarising the main signalling pathways around BRAF was constructed from the literature. This model is then personalized using cell line data (from Cell Model Passports). This results in as many personalized mechanistic models as there are cell lines. Simulations are then performed with these personalized models to see how they respond to BRAF node inhibition. This document is at the end of this pipeline since it will import the results of these simulations and compare them to the sensitivities observed experimentally.
We first import simulation results from personalized models and drug/CRISPR screening files.
## [1] "All imports OK"
We retrieve data from different screenings and with different metrics. Is there any consistence between all these values? We plot the correlation for the different measures across datasets and metrics. We focus on BRAF, TP53 and PIK3CA, the three targets mentioned in the article
Indeed, we observe some correlation clusters. When there are different drugs inhibiting the same target, their sensitivities are highly correlated. Besides, drug and CRISPR sensitivity outcomes are anti-correlated, especially for BRAF. This makes sense because of the definition of the metrics involved (sensitive cell lines have low AUC in drug screening and high scaled Bayesian factor in CRISPR).
Now we re-process the data to ease following analyses. Since the model contains a read-out node named Proliferation we will use it as a proxy to validate with experimental sensitivities.
We also define a normalised variable based on Proliferation level without any drug inhibition (i.e \(Proliferation_{normalised} = Proliferation_{withDrug} / Proliferation_{withoutDrug}\))
We first compare the Proliferation outcome from personalized models with the experimental sensitivities to BRAF inhibition.
We use different Proliferation proxies: the one from models without drug (resp. CRISPR) inhibition, the one from models with drug (resp. CRISPR) inhibition, and the normalised one. We also compare different personalization methods: with mutations only, with RNA only, and with mutations and RNA together.
On the following plot correlation coefficients are shown only for significant correlations. B panels correspond to Drugs screening and C panels to CRISPR screenings.
Some interesting points:
Here are additional plots for other targets;
For drugs we will focus on one drug only which is PLX-4720 (very similar to Dabrafenib in many aspects, no particular criterion to distinguish) and AUC metric (less sensitive to extrapolation). For CRISPR screening we will focus on CC2 dataset, more balanced in CM and CRC. For output we will also focus on normalised Proliferation scores:
Here is the pruned version of the plot for publication:
Now we would like to have a look at more precise patters beyond correlation coefficients.
First we propose some simple static plots to observe the correlation patterns. In the following part, some interactive plot will allow a better investigation.
Here is the version for publication:
And here is the version with table to replace interactive plot in the static publication:
Additional plot for p53 and PI3K:
We can have a deeper look at scatter plot with interactive settings
Let’s generate each column as an interactive plot, first with drugs and then with CRISPR:
And a last interactive plot to visualize the benefit of RNA addition for CRISPR prediction:
## [1] "Done"
## [1] "Computation time:"
## 109.065 sec elapsed